TY  - JOUR
PY  - 2016//
TI  - Using embedded sensors in independent living to predict gait changes and falls
JO  - Western journal of nursing research
A1  - Phillips, Lorraine J.
A1  - DeRoche, Chelsea B.
A1  - Rantz, Marilyn
A1  - Alexander, Gregory L.
A1  - Skubic, Marjorie
A1  - Despins, Laurel
A1  - Abbott, Carmen C.
A1  - Harris, Bradford H.
A1  - Galambos, Colleen
A1  - Koopman, Richelle
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - This study explored using big data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell (n = 13) and those who did not fall (n = 10). We analyzed associations between participants' fall events (n = 69) and pre-fall changes in in-home gait speed and stride length (n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall (p <.0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. <br><br>RESULTS demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.<br><br>© The Author(s) 2016.<p />  <p>Language: en</p>
LA  - en
SN  - 0193-9459
UR  - http://dx.doi.org/10.1177/0193945916662027
ID  - ref1
ER  -